Troubleshooting is a pervasive challenge in engineering practice, yet few validated learning frameworks explicitly target the development of troubleshooting expertise. This NSF-funded Building Capacity for STEM Education Research (BCSER) project aims to design a learning environment that cultivates adaptive troubleshooting skills, framing troubleshooting as a diagnosis-solution problem type.
During Year 1, we investigated the key cognitive competencies underlying successful troubleshooting. We conducted in-depth interviews with 18 engineers, each with over 10 years of industrial (n=14) or academic research experience (n=4), to examine how they perceive and manage complexity in troubleshooting. The experts’ reflections revealed four primary categories of complexity, including system representation, information ambiguity, situational, and stakeholder types. Our analysis further showed how experts draw on prefigurative schemas to regulate knowledge types and reasoning strategies when addressing complex, ill-structured problems.
We further examined experts’ reasoning processes using think-aloud problem-solving protocols focused on specific complexity categories. From these findings, we developed a conceptual model of troubleshooting expertise and identified the initial design elements for a learning environment that supports adaptive reasoning and knowledge flexibility in complex problem contexts.
http://orcid.org/https://0000-0001-7905-421X
Worcester Polytechnic Institute
[biography]
The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026